Compatible with Keras 2.4.3

.travis.yml

@@ -1,19 +1,29 @@
+dist: xenial
 language: python
-python:
-  - "2.7"
-  - "3.6"
+python: "3.6"
+env:
+  global:
+    - COVERALLS_PARALLEL=true
+  matrix:
+    - KERAS_BACKEND=tensorflow
+    - KERAS_BACKEND=tensorflow TF_KERAS=1
 install:
+  - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;
+  - bash miniconda.sh -b -p $HOME/miniconda
+  - export PATH="$HOME/miniconda/bin:$PATH"
+  - conda config --set always_yes yes --set changeps1 no
+  - conda update -q conda
+  - conda info -a
+  - conda create -q -n test-environment python=$TRAVIS_PYTHON_VERSION
+  - source activate test-environment
+  - export LD_LIBRARY_PATH=$HOME/miniconda/envs/test-environment/lib/:$LD_LIBRARY_PATH
   - pip install --upgrade pip
-  - pip install numpy
-  - pip install tensorflow
-  - pip install keras
-  - pip install pycodestyle
-  - pip install coverage
+  - pip install -r requirements.txt
+  - pip install -r requirements-dev.txt
   - pip install coveralls
-  - pip install keras-word-char-embd
-before_script:
-  - bash lint.sh
 script:
-  - bash test.sh
+  - ./test.sh
 after_success:
   coveralls
+notifications:
+  webhooks: https://coveralls.io/webhook

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2018 PoW
+Copyright (c) 2020 Zhao HG
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

keras_wc_embd/__init__.py

@@ -1,2 +1,4 @@
 from .word_char_embd import *
 from .wrapper import WordCharEmbd
+
+__version__ = '0.21.0'

keras_wc_embd/backend.py

@@ -0,0 +1,33 @@
+import os
+from distutils.util import strtobool
+
+__all__ = [
+    'keras', 'utils', 'activations', 'applications', 'backend', 'datasets',
+    'layers', 'preprocessing', 'wrappers', 'callbacks', 'constraints', 'initializers',
+    'metrics', 'models', 'losses', 'optimizers', 'regularizers', 'TF_KERAS',
+]
+
+TF_KERAS = strtobool(os.environ.get('TF_KERAS', '0'))
+
+if TF_KERAS:
+    import tensorflow as tf
+    keras = tf.keras
+else:
+    import keras
+
+utils = keras.utils
+activations = keras.activations
+applications = keras.applications
+backend = keras.backend
+datasets = keras.datasets
+layers = keras.layers
+preprocessing = keras.preprocessing
+wrappers = keras.wrappers
+callbacks = keras.callbacks
+constraints = keras.constraints
+initializers = keras.initializers
+metrics = keras.metrics
+models = keras.models
+losses = keras.losses
+optimizers = keras.optimizers
+regularizers = keras.regularizers

keras_wc_embd/word_char_embd.py

@@ -1,7 +1,7 @@
 import codecs
 import numpy
-import keras
-import keras.backend as K
+from .backend import keras
+from .backend import backend as K
 
 __all__ = [
     'MaskedConv1D', 'MaskedFlatten',

setup.py

@@ -1,30 +1,43 @@
+import os
+import re
 import codecs
 from setuptools import setup, find_packages
 
+current_path = os.path.abspath(os.path.dirname(__file__))
 
-with codecs.open('README.md', 'r', 'utf8') as reader:
-    long_description = reader.read()
 
+def read_file(*parts):
+    with codecs.open(os.path.join(current_path, *parts), 'r', 'utf8') as reader:
+        return reader.read()
 
-with codecs.open('requirements.txt', 'r', 'utf8') as reader:
-    install_requires = list(map(lambda x: x.strip(), reader.readlines()))
+
+def get_requirements(*parts):
+    with codecs.open(os.path.join(current_path, *parts), 'r', 'utf8') as reader:
+        return list(map(lambda x: x.strip(), reader.readlines()))
+
+
+def find_version(*file_paths):
+    version_file = read_file(*file_paths)
+    version_match = re.search(r"^__version__ = ['\"]([^'\"]*)['\"]", version_file, re.M)
+    if version_match:
+        return version_match.group(1)
+    raise RuntimeError('Unable to find version string.')
 
 
 setup(
     name='keras-word-char-embd',
-    version='0.20.0',
+    version=find_version('keras_wc_embd', '__init__.py'),
     packages=find_packages(),
     url='https://github.com/CyberZHG/keras-word-char-embd',
     license='MIT',
     author='CyberZHG',
-    author_email='CyberZHG@gmail.com',
+    author_email='CyberZHG@users.noreply.github.com',
     description='Concatenate word and character embeddings in Keras',
-    long_description=long_description,
+    long_description=read_file('README.md'),
     long_description_content_type='text/markdown',
-    install_requires=install_requires,
+    install_requires=get_requirements('requirements.txt'),
     classifiers=(
-        "Programming Language :: Python :: 2.7",
-        "Programming Language :: Python :: 3.6",
+        "Programming Language :: Python :: 3",
         "License :: OSI Approved :: MIT License",
         "Operating System :: OS Independent",
     ),

test.sh

@@ -1,2 +1,3 @@
 #!/usr/bin/env bash
-nosetests --with-coverage --cover-erase --cover-html --cover-html-dir=htmlcov --cover-package="keras_wc_embd" tests
+pycodestyle --max-line-length=120 keras_wc_embd tests && \
+    nosetests --nocapture --with-coverage --cover-erase --cover-html --cover-html-dir=htmlcov --cover-package=keras_wc_embd --with-doctest

tests/test_get_embedding_layer.py

@@ -16,9 +16,9 @@ def test_shape_rnn(self):
             char_hidden_dim=75,
         )
         self.assertEqual(len(inputs), 2)
-        self.assertEqual(inputs[0]._keras_shape, (None, None))
-        self.assertEqual(inputs[1]._keras_shape, (None, None, 5))
-        self.assertEqual(embd_layer._keras_shape, (None, None, 300))
+        self.assertEqual(inputs[0].shape.as_list(), [None, None])
+        self.assertEqual(inputs[1].shape.as_list(), [None, None, 5])
+        self.assertEqual(embd_layer.shape.as_list(), [None, None, 300])
         inputs, embd_layer = get_embedding_layer(
             word_dict_len=3,
             char_dict_len=5,
@@ -29,9 +29,9 @@ def test_shape_rnn(self):
             char_hidden_layer_type='gru',
         )
         self.assertEqual(len(inputs), 2)
-        self.assertEqual(inputs[0]._keras_shape, (None, None))
-        self.assertEqual(inputs[1]._keras_shape, (None, None, 7))
-        self.assertEqual(embd_layer._keras_shape, (None, None, 600))
+        self.assertEqual(inputs[0].shape.as_list(), [None, None])
+        self.assertEqual(inputs[1].shape.as_list(), [None, None, 7])
+        self.assertEqual(embd_layer.shape.as_list(), [None, None, 600])
 
     def test_pre_trained_shape(self):
         get_embedding_layer(
@@ -85,9 +85,9 @@ def test_shape_cnn(self):
             char_mask_zero=True,
         )
         self.assertEqual(len(inputs), 2)
-        self.assertEqual(inputs[0]._keras_shape, (None, None))
-        self.assertEqual(inputs[1]._keras_shape, (None, None, 7))
-        self.assertEqual(embd_layer._keras_shape, (None, None, 450))
+        self.assertEqual(inputs[0].shape.as_list(), [None, None])
+        self.assertEqual(inputs[1].shape.as_list(), [None, None, 7])
+        self.assertEqual(embd_layer.shape.as_list(), [None, None, 450])
 
     def test_custom(self):
         layers = [
@@ -117,9 +117,9 @@ def test_custom(self):
             char_mask_zero=False,
         )
         self.assertEqual(len(inputs), 2)
-        self.assertEqual(inputs[0]._keras_shape, (None, None))
-        self.assertEqual(inputs[1]._keras_shape, (None, None, 7))
-        self.assertEqual(embd_layer._keras_shape, (None, None, 350))
+        self.assertEqual(inputs[0].shape.as_list(), [None, None])
+        self.assertEqual(inputs[1].shape.as_list(), [None, None, 7])
+        self.assertEqual(embd_layer.shape.as_list(), [None, None, 350])
         inputs, embd_layer = get_embedding_layer(
             word_dict_len=3,
             char_dict_len=5,
@@ -130,9 +130,9 @@ def test_custom(self):
             char_mask_zero=False,
         )
         self.assertEqual(len(inputs), 2)
-        self.assertEqual(inputs[0]._keras_shape, (None, None))
-        self.assertEqual(inputs[1]._keras_shape, (None, None, 7))
-        self.assertEqual(embd_layer._keras_shape, (None, None, 330))
+        self.assertEqual(inputs[0].shape.as_list(), [None, None])
+        self.assertEqual(inputs[1].shape.as_list(), [None, None, 7])
+        self.assertEqual(embd_layer.shape.as_list(), [None, None, 330])
 
     def test_not_implemented(self):
         with self.assertRaises(NotImplementedError):

tests/test_wrapper.py

@@ -1,7 +1,7 @@
 import unittest
 import os
-import keras
 import numpy as np
+from keras_wc_embd.backend import keras
 from keras_wc_embd import WordCharEmbd